In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and m...In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and multiple user detection techniques,our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings,where the upper bound on performance is 1.Moreover,a lower bound on throughput performance is derived,which is tight in some parameter settings and can be used to approximate theoretical performance.Simulation results validate our analysis and confirm the advantages of our proposed scheme.展开更多
The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of informatio...The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of information and energy transfer.In this research,a hybrid active/passive IRS-assisted SWIPT system is proposed.Specifically,an active IRS(AIRS)and a passive IRS(PIRS)are deployed in the SWIPT system to facilitate a multiantenna base station(BS)in simultaneously delivering information and energy to multiple information users(IUs)and energy users(EUs).The objective is to maximize the sum throughput by jointly optimizing the transmitter beamforming and the reflection coefficient matrices of the AIRS and the PIRS while satisfying the transmitter power constraints,the energy harvesting(EH)requirements of EUs,and the AIRS amplification power limitations.However,the optimization variables are highly coupled and cannot be solved directly.To tackle this complex problem,we propose an efficient algorithm based on alternating optimization(AO)and semi-definite relaxation(SDR)techniques to obtain high-quality solutions.Simulation results demonstrate that the hybrid active/passive IRSassisted SWIPT system significantly enhances throughput performance and outperforms benchmark systems.展开更多
Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single...Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.展开更多
To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized pur...To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.展开更多
Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo developm...Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.展开更多
In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausti...In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.展开更多
Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Me...Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.展开更多
Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous ...Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.展开更多
面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组...面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。展开更多
文摘In this paper,we propose a random access scheme termed sign-compute diversity slotted ALOHA(SCDSA).The SCDSA scheme combines diversity transmission with compute-and-forward.Without considering the capture effect and multiple user detection techniques,our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings,where the upper bound on performance is 1.Moreover,a lower bound on throughput performance is derived,which is tight in some parameter settings and can be used to approximate theoretical performance.Simulation results validate our analysis and confirm the advantages of our proposed scheme.
基金National Natural Science Foundation of China(No.62301141)。
文摘The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of information and energy transfer.In this research,a hybrid active/passive IRS-assisted SWIPT system is proposed.Specifically,an active IRS(AIRS)and a passive IRS(PIRS)are deployed in the SWIPT system to facilitate a multiantenna base station(BS)in simultaneously delivering information and energy to multiple information users(IUs)and energy users(EUs).The objective is to maximize the sum throughput by jointly optimizing the transmitter beamforming and the reflection coefficient matrices of the AIRS and the PIRS while satisfying the transmitter power constraints,the energy harvesting(EH)requirements of EUs,and the AIRS amplification power limitations.However,the optimization variables are highly coupled and cannot be solved directly.To tackle this complex problem,we propose an efficient algorithm based on alternating optimization(AO)and semi-definite relaxation(SDR)techniques to obtain high-quality solutions.Simulation results demonstrate that the hybrid active/passive IRSassisted SWIPT system significantly enhances throughput performance and outperforms benchmark systems.
基金the financial support of the National Natural Science Foundation of China(No.22168009)。
文摘Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.
基金the National Natural Science Foundation of China (No. 22306146)the PhD Scientific Research Startup Foundation of Xihua University (No. RX2200002003) for their financial support。
文摘To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.
基金supported by National Natural Science Foundation of China (32172747 and 32425052)
文摘Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.
文摘In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4604800,2021YFA1202300)the Natural and Science Foundation of China(Grant Nos.52201041,52275331,52205358)+1 种基金the Key Research and Development Program of Hubei Province(Nos.2024BCB091,2022CFA031)the Hong Kong Scholars Program(No.XJ2022014)。
文摘Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.
基金supported by Shanghai Municipal Commission of Science and Technology,China(Grant No.:19XD1400300)the National Natural Science Foundation of China(Grant Nos.:821040821,82273867,and 82030107).
文摘Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.
文摘面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。
